Fuzzy C-Mean Clustering Algorithms Based on Picard Iteration and Particle Swarm Optimization

Authors:
Hsiang-Chuan Liu;Jeng-Ming Yih;Der-Bang Wu;Shin-Wu Liu
Affiliations:
-;-;-;-
Venue:
ETTANDGRS '08 Proceedings of the 2008 International Workshop on Education Technology and Training & 2008 International Workshop on Geoscience and Remote Sensing - Volume 02
Year:
2008

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Abstract

The popular fuzzy c-means algorithm (FCM) converges to a local minimum of the objective function. Hence, different initializations may lead to different results. The important issue is how to avoid getting a bad local minimum value to improve the cluster accuracy. The particle swarm optimization (PSO) is a popular and robust strategy for optimization problems. But the main difficulty in applying PSO to real-world applications is that PSO usually need a large number of fitness evaluations before a satisfying result can be obtained. In this paper, the improved new algorithm, “Fuzzy C-Mean based on Picard iteration and PSO (PPSO-FCM)”, is proposed. Two real data sets were applied to prove that the performance of the PPSO-FCM algorithm is better than the conventional FCM algorithm and the PSO-FCM algorithm.